no code implementations • 8 Mar 2024 • Lucas Farndale, Chris Walsh, Robert Insall, Ke Yuan
Multimodal self-supervised representation learning has consistently proven to be a highly effective method in medical image analysis, offering strong task performance and producing biologically informed insights.
1 code implementation • 4 Mar 2024 • Zhengqi Xu, Ke Yuan, Huiqiong Wang, Yong Wang, Mingli Song, Jie Song
Furthermore, the visualization of the merged model within the multi-task loss landscape reveals that MuDSC enables the merged model to reside in the overlapping segment, featuring a unified lower loss for each task.
no code implementations • 4 Dec 2023 • Lucas Farndale, Robert Insall, Ke Yuan
We present TriDeNT, a novel self-supervised method for utilising privileged data that is not available during inference to improve performance.
no code implementations • 19 Mar 2023 • Lucas Farndale, Robert Insall, Ke Yuan
Medical imaging technologies are generating increasingly large amounts of high-quality, information-dense data.
1 code implementation • 4 May 2022 • Adalberto Claudio Quiros, Nicolas Coudray, Anna Yeaton, Xinyu Yang, Bojing Liu, Hortense Le, Luis Chiriboga, Afreen Karimkhan, Navneet Narula, David A. Moore, Christopher Y. Park, Harvey Pass, Andre L. Moreira, John Le Quesne, Aristotelis Tsirigos, Ke Yuan
Definitive cancer diagnosis and management depend upon the extraction of information from microscopy images by pathologists.
1 code implementation • 4 Aug 2021 • Adalberto Claudio Quiros, Nicolas Coudray, Anna Yeaton, Wisuwat Sunhem, Roderick Murray-Smith, Aristotelis Tsirigos, Ke Yuan
We present an adversarial learning model to extract feature representations of cancer tissue, without the need for manual annotations.
no code implementations • 2 May 2021 • Shuai Peng, Ke Yuan, Liangcai Gao, Zhi Tang
Large-scale pre-trained models like BERT, have obtained a great success in various Natural Language Processing (NLP) tasks, while it is still a challenge to adapt them to the math-related tasks.
no code implementations • 24 Apr 2021 • Ke Yuan, Zuoyu Yan, Yibo Li, Liangcai Gao, Zhi Tang
In the Selector, a Topic Relation Graph (TRG) is proposed to obtain the relevant documents which contain the comprehensive information of math expressions.
no code implementations • 23 Dec 2020 • Zuoyu Yan, Xiaode Zhang, Liangcai Gao, Ke Yuan, Zhi Tang
Despite the recent advances in optical character recognition (OCR), mathematical expressions still face a great challenge to recognize due to their two-dimensional graphical layout.
1 code implementation • 13 Apr 2020 • Adalberto Claudio Quiros, Roderick Murray-Smith, Ke Yuan
We present a deep generative model that learns to simulate high-fidelity cancer tissue images while mapping the real images onto an interpretable low dimensional latent space.
1 code implementation • 27 Nov 2019 • Ke Yuan, Dafang He, Zhuoren Jiang, Liangcai Gao, Zhi Tang, C. Lee Giles
Compared to conventional summarization tasks, this task has two extra and essential constraints: 1) Detailed math questions consist of text and math equations which require a unified framework to jointly model textual and mathematical information; 2) Unlike text, math equations contain semantic and structural features, and both of them should be captured together.
1 code implementation • MIDL 2019 • Adalberto Claudio Quiros, Roderick Murray-Smith, Ke Yuan
We show that our model generates high quality images, with a FID of 16. 65 (breast cancer) and 32. 05 (colorectal cancer).